R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is the base for many R packages listed in https://cran.r-project.org/

This software is also referenced in ORMS.

References in zbMATH (referenced in 2911 articles , 6 standard articles )

Showing results 1 to 20 of 2911.
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  1. Abdi, Hervé; Beaton, Derek: Principal component and correspondence analyses using R (to appear) (2017)
  2. Arangala, Crista; Yokley, Karen A.: Exploring calculus. Labs and projects with Mathematica (2017)
  3. Bolstad, William M.; Curran, James M.: Introduction to Bayesian statistics (2017)
  4. Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)
  5. Fan, Yanan; de Micheaux, Pierre Lafaye; Penev, Spiridon; Salopek, Donna: Multivariate nonparametric test of independence (2017)
  6. Greenacre, Michael: Correspondence analysis in practice (2017)
  7. Mathur, Sunil K.: Statistical bioinformatics with R (to appear) (2017)
  8. Rogers, Simon; Girolami, Mark: A first course in machine learning (2017)
  9. von Auer, Ludwig; Hoffmann, Sönke: Econometrics. The R work book (2017)
  10. Acosta, Jonathan; Osorio, Felipe; Vallejos, Ronny: Effective sample size for line transect sampling models with an application to marine macroalgae (2016)
  11. Adragni, Kofi P.; Al-Najjar, Elias; Martin, Sean; Popuri, Sai K.; Raim, Andrew M.: Group-wise sufficient dimension reduction with principal fitted components (2016)
  12. Alexandrowicz, Rainer W.; Draxler, Clemens: Testing the Rasch model with the conditional likelihood ratio test: sample size requirements and bootstrap algorithms (2016)
  13. Alfaro, César; Cano-Montero, Javier; Gómez, Javier; Moguerza, Javier M.; Ortega, Felipe: A multi-stage method for content classification and opinion mining on weblog comments (2016)
  14. Alonso, Ariel; Der Elst, Wim van; Molenberghs, Geert; Buyse, Marc; Burzykowski, Tomasz: An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference (2016)
  15. Amerise, Ilaria L.; Tarsitano, Agostino: Combining dissimilarity matrices by using rank correlations (2016)
  16. Anderson-Bergman, Clifford; Yu, Yaming: Computing the log concave NPMLE for interval censored data (2016)
  17. Anderson, Gordon; Farcomeni, Alessio; Pittau, Maria Grazia; Zelli, Roberto: A new approach to measuring and studying the characteristics of class membership: examining poverty, inequality and polarization in urban China (2016)
  18. Antoneli, Fernando; Ferreira, Renata C.; Briones, Marcelo R.S.: A model of gene expression based on random dynamical systems reveals modularity properties of gene regulatory networks (2016)
  19. Arciga Alejandre, Martin P.; Ariza Hernandez, Francisco J.; Sanchez Ortiz, Jorge: Stochastic evolution equation with Riesz-fractional derivative and white noise on the half-line (2016)
  20. Arlot, Sylvain; Genuer, Robin: Comments on: “A random forest guided tour” (2016)

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Further publications can be found at: http://journal.r-project.org/